论文标题
全球对比度掩盖自动编码器是强大的病理表示学习者
Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners
论文作者
论文摘要
基于数字病理切片扫描技术，以深度学习为代表的人工智能算法在计算病理学领域取得了显着的结果。与其他医学图像相比，病理图像更难以注释，因此，极其缺乏可用的数据集来进行监督学习以训练健壮的深度学习模型。在本文中，我们提出了一种自我监督的学习（SSL）模型，即全球对比度掩盖自动编码器（GCMAE），该模型可以训练编码器以具有代表病理图像的局部 - 全球特征的能力，还显着提高了跨数据集的传输学习的性能。在这项研究中，通过使用总共使用三种不同疾病特异性苏木精和曙红（HE）染色的病理数据集的广泛实验来证明GCMAE学习可迁移表示形式的能力：CamelyOn16，NCTCRC和Breakhis。此外，这项研究设计了一种基于临床应用的GCMAE的有效自动病理诊断过程。本文的源代码可在https://github.com/staruniversus/gcmae上公开获得。
Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology. Compared to other medical images, pathology images are more difficult to annotate, and thus, there is an extreme lack of available datasets for conducting supervised learning to train robust deep learning models. In this paper, we propose a self-supervised learning (SSL) model, the global contrast-masked autoencoder (GCMAE), which can train the encoder to have the ability to represent local-global features of pathological images, also significantly improve the performance of transfer learning across data sets. In this study, the ability of the GCMAE to learn migratable representations was demonstrated through extensive experiments using a total of three different disease-specific hematoxylin and eosin (HE)-stained pathology datasets: Camelyon16, NCTCRC and BreakHis. In addition, this study designed an effective automated pathology diagnosis process based on the GCMAE for clinical applications. The source code of this paper is publicly available at https://github.com/StarUniversus/gcmae.
